![](/images/graphics-bg.png)
A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction
Joint Authors
Le, Tuong
Baik, Sung Wook
Lee, Mi Young
Vo, Minh Thanh
Vo, Bay
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-08-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments.
Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world.
Due to the class imbalance problem occurring in the bankruptcy datasets, several special techniques would be used to improve the prediction performance.
Oversampling technique and cost-sensitive learning framework are two common methods for dealing with class imbalance problem.
Using oversampling techniques and cost-sensitive learning framework independently also improves predictability.
However, for datasets with very small balancing ratios, combining two above techniques will produce the better results.
Therefore, this study develops a hybrid approach using oversampling technique and cost-sensitive learning, namely, HAOC for bankruptcy prediction on the Korean Bankruptcy dataset.
The first module of HAOC is oversampling module with an optimal balancing ratio found in the first experiment that will give the best overall performance for the validation set.
Then, the second module uses the cost-sensitive learning model, namely, CBoost algorithm to bankruptcy prediction.
The experimental results show that HAOC will give the best performance value for bankruptcy prediction compared with the existing approaches.
American Psychological Association (APA)
Le, Tuong& Vo, Minh Thanh& Vo, Bay& Lee, Mi Young& Baik, Sung Wook. 2019. A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction. Complexity،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132954
Modern Language Association (MLA)
Le, Tuong…[et al.]. A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction. Complexity No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1132954
American Medical Association (AMA)
Le, Tuong& Vo, Minh Thanh& Vo, Bay& Lee, Mi Young& Baik, Sung Wook. A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction. Complexity. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132954
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132954